References
- Aarnio, Aulis. The Rational as Reasonable: A Treatise on Legal Justification (Springer Science & Business Media 1986): 22.
- Alexander, Larry, Moore, Michael. “Deontological Ethics.” In The Stanford Encyclopedia of Philosophy edited by Edward N. Zalta (Winter 2020, Metaphysics Research Lab, Stanford University 2020).
- Araujo, Theo, et al. “In AI We Trust? Perceptions about Automated Decision-Making by Artificial Intelligence.” AI & Society 35 (2020): 611, 616.
- Barocas, Solon, Selbst Andrew D, “Big Data's Disparate Impact.” California Law Review 104 (2016): 671, 692.
- Biran Or, Cotton, Courtenay. “Explanation and Justification in Machine Learning: A Survey.” /paper/Explanation-and-Justification-in-Machine-Learning-%3A-Biran-Cotton/02e2e79a77d8aabc1af1900ac80ceebac20abde4.
- Brennan-Marquez, Kiel. “Plausible Cause’: Explanatory Standards in the Age of Powerful Machines.” Vanderbilt Law Review 70, no. 53 (2017).
- Brkan, Maja. “The Essence of the Fundamental Rights to Privacy and Data Protection: Finding the Way Through the Maze of the CJEU's Constitutional Reasoning.” German Law Journal 20 (2019): 864.
- Butterworth, Michael. “The ICO and Artificial Intelligence: The Role of Fairness in the GDPR Framework.” Computer Law & Security Review 34 (2018): 257.
- Clifford, Damian, Ausloos, Jeff. “Data Protection and the Role of Fairness.” Yearbook of European Law 37 (2018): 130.
- Dressel, Julia, Farid, Hany. “The Accuracy, Fairness, and Limits of Predicting Recidivism.” Science Advances 4 (2018): eaao5580.
- Dwork, Cynthia, Mulligan, Deirdre K. “It's Not Privacy, and It's Not Fair.” Stanford Law Review 6 (2013): 66.
- Edwards, Lilian, Veale, Michael. “Slave to the Algorithm? Why a ‘Right to an Explanation’ Is Probably Not the Remedy You Are Looking For,” 16 Duke Law & Technology Review 18 (2017).
- Edwards, Lilian, Veale, Michael. “Enslaving the Algorithm: From a ‘Right to an Explanation’ to a ‘Right to Better Decisions’?” 16 IEEE Security & Privacy 46 (2018).
- Galhotra, Sainyam, Brun, Yuriy, Meliou, Alexandra. “Fairness Testing: Testing Software for Discrimination.” Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering—ESEC/FSE 2017, (ACM, 2017) http://dl.acm.org/citation.cfm?doid=3106237.3106277.
- Goodman, Bryce, Flaxman, Seth. “EU Regulations on Algorithmic Decision-Making and a ‘Right to Explanation’” arXiv:1606.08813 [cs, stat] http://arxiv.org/abs/1606.08813, accessed 30 June 2018.
- Goodman, Bryce. A Step Towards Accountable Algorithms?: Algorithmic Discrimination and the European Union General Data Protection. (2016).
- Hamon, Ronan and others. “Impossible Explanations? Beyond Explainable AI in the GDPR from a COVID-19 Use Case Scenario.” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Association for Computing Machinery 2021).
- Hazen, Benjamin T., et al. “Data Quality for Data Science, Predictive Analytics, and Big Data in Supply Chain Management: An Introduction to the Problem and Suggestions for Research and Applications.” International Journal of Production Economics 154 (2014): 72.
- Henin, Clément, Le Métayer, Daniel. “A Framework to Contest and Justify Algorithmic Decisions.” [2021] AI and Ethics.
- Hildebrandt, Mireille. Law for Computer Scientists and Other Folk (Oxford University Press 2020): 267.
- Hildebrandt, Mireille. “Profile Transparency by Design? Re-Enabling Double Contingency.” available at https://works.bepress.com/mireille_hildebrandt/63/.
- Horowitz, Donald L. “Justification and Excuse in the Program of the Criminal Law.” Law and Contemporary Problems 49 (1986): 109.
- Hutton, Luke, Henderson, Tristan. “Beyond the EULA: Improving Consent for Data Mining,” In Transparent Data Mining for Big and Small Data edited by Tania Cerquitelli, Daniele Quercia, and Frank Pasquale (eds.), (Springer, New York 2017): 147 at 162.
- Kaminski, Margot E. “The Right to Explanation, Explained.” 34 Berkeley Technology Law Journal (2019): 189.
- Kaminski, Margot E., Malgieri, Gianclaudio. “Multi-Layered Explanation from Algorithmic Impact Assessments in the GDPR.” FAT 2020 Proceedings (ACM Publishing, 2020).
- Kaminski, Margot E., Malgieri, Gianclaudio. “Algorithmic Impact Assessments under the GDPR: Producing Multi-Layered Explanations.” 19–28 University of Colorado Law Legal Studies Research Paper available at https://papers.ssrn.com/abstract=3456224.
- Kaminski, Margot. “Binary Governance: Lessons from the GDPR's Approach to Algorithmic Accountability.” 92 Southern California Law Review 1529 (2019):12–17.
- Katyal, Sonia K. “Private Accountability in the Age of Artificial Intelligence.” UCLA Law Review 66 (2019): 88.
- Kim, Pauline T. “Data-Driven Discrimination at Work.” 58 Wm. & Mary L. Rev. (2017): 857.
- Kloza, Dariusz, et al. “Data Protection Impact Assessment in the European Union: Developing a Template for a Report from the Assessment Process.” (LawArXiv 2020) DPiaLab Policy Brief 29 available at https://osf.io/7qrfp.
- Kroll, Joshua et al. “Accountable Algorithms.” University of Pennsylvania Law Review 165 (2017): 633.
- Lepri, Bruno, et al., “Fair, Transparent, and Accountable Algorithmic Decision-Making Processes.” Philosophy & Technology 31 (2018): 611.
- Lipton, Zachary C. “The Mythos of Model Interpretability.” Communications of the ACM 61 (2018): 36.
- Lodder, Arno R. Dialaw: On Legal Justification and Dialogical Models of Argumentation (1999º edizione, Kluwer Academic Publishers, 1999).
- Loi, Michele, Ferrario, Andrea, Viganò, Eleonora. “Transparency as Design Publicity: Explaining and Justifying Inscrutable Algorithms.” In Ethics and Information Technology, https://doi.org/10.1007/s10676-020-09564-w.
- Malgieri, Gianclaudio, Comandé, Giovanni. “Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation.” International Data Privacy Law 7, no. 4 (2017): 243–65.
- Malgieri, Gianclaudio. “Automated Decision-Making in the EU Member States: The Right to Explanation and Other ‘Suitable Safeguards’ in the National Legislations.” Computer Law & Security Review 35, no. 105327 (2019): 9–11.
- Malgieri, Gianclaudio. “The Concept of Fairness in the GDPR: A Linguistic and Contextual Interpretation.” Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Association for Computing Machinery 2020) available at https://doi.org/10.1145/3351095.3372868, accessed 29 January 2020.
- Malgieri, Gianclaudio, Niklas, Jedrzej. “The Vulnerable Data Subject.” 37 Computer Law & Security Review (2020).
- Milaj, Jonida. “Privacy, Surveillance, and the Proportionality Principle: The Need for a Method of Assessing Privacy Implications of Technologies Used for Surveillance.” International Review of Law, Computers & Technology 30 (2016): 115, 116.
- Miller, Tim. “Explanation in Artificial Intelligence: Insights from the Social Sciences.” 267 Artificial Intelligence 1, (2019).
- Mortier Richard and others. “Human-Data Interaction.” In The Interaction Design Foundation (ed), The Encyclopedia of Human-Computer Interaction, (2nd edition, The Interaction Design Foundation 2015).
- Moser, Paul K “Justification in the Natural Sciences.” The British Journal for the Philosophy of Science 39 (1991): 557–75.
- Oprișiu, Raluca. “Reversal of ‘the Burden of Proof’ in Data Protection | Lexology.” available at https://www.lexology.com/library/detail.aspx?g=e9e8c734-23d9-41bb-a723-5d664b3c86cc.
- Petkova, Bilyana, Hacker, Philipp. “Reining in the Big Promise of Big Data: Transparency, Inequality, and New Regulatory Frontiers.” Lecturer and Other Affiliate Scholarship Series available at https://digitalcommons.law.yale.edu/ylas/13 (2016).
- Ramamurthy, Karthikeyan Natesan, et al. “Model Agnostic Multilevel Explanations.” available at https://arxiv.org/abs/2003.06005v1, accessed 25 March 2020.
- Reisman, Dillon, et al. Algorithm Impact Assessment: A Practical Framework for Public Agency Accountability. (AI Now Institute: 2018).
- Roig, Antoni. “Safeguards for the Right Not to Be Subject to a Decision Based Solely on Automated Processing (Article 22 GDPR).” European Journal of Law and Technology 8 (2018).
- Rudin, Cynthia. “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.” Nature Machine Intelligence 1 (2019): 206, 207.
- Selbst, Andrew D., Powles, Julia. “Meaningful Information and the Right to Explanation.” International Data Privacy Law 7, no. 4 (2017): 233–42.
- Selbst, Andrew D. “Disparate Impact in Big Data Policing.” Georgia Law Review 52 (2018): 109; Reisman et al., (n 76).
- Selbst, Andrew D., et al. “Fairness and Abstraction in Sociotechnical Systems.” Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM, 2019) http://doi.acm.org/10.1145/3287560.3287598.
- Selbst, Andrew D., Barocas, Solon. “The Intuitive Appeal of Explainable Machines.” 87 Fordham Law Review 1085 (2018).
- Smith, J. C. Justification and Excuse in the Criminal Law (Stevens 1989).
- Tyler, Tom R. “Procedural Justice, Legitimacy, and the Effective Rule of Law.” Crime and Justice 30, no. 283, (2003): 317–18.
- Veale, Michael, Edwards, Lilian. “Clarity, Surprises, and Further Questions in the Article 29 Working Party Draft Guidance on Automated Decision-Making and Profiling.” Computer Law & Security Review 34 (2018): 398.
- Wachter, Sandra, Mittelstadt, Brent, Floridi, Luciano. “Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation.” International Data Privacy Law 7, no. 2 (2017): 76–99.
- Wachter, Sandra, Mittelstadt, Brent, Russell, Chris. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harvard Journal of Law & Technology 31, no. 2 (2018).
- Wachter, Sandra. Affinity Profiling and Discrimination by Association in Online Behavioural Advertising (Social Science Research Network 2019) SSRN Scholarly Paper ID 3388639 https://papers.ssrn.com/abstract=3388639.
- Wachter, Sandra, Mittelstadt, Brent. “A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI.” Columbia Business Law Review 2 (2019).